T 71: Data analysis, Information technology III
Wednesday, March 17, 2021, 16:00–18:15, Tu
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16:00 |
T 71.1 |
Usage of neural networks in photon identification in ATLAS — •Florian Kirfel and Oleh Kivernyk
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16:15 |
T 71.2 |
Studies of modern machine learning methods for tau lepton identification with the CMS detector — •Andrew Issac, Günter Quast, Roger Wolf, Stefan Wunsch, and Sebastian Brommer
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16:30 |
T 71.3 |
Adversarial Neural Network-based shape calibrations of observables for jet-tagging at CMS — Martin Erdmann, •Benjamin Fischer, Jan Middendorf, Dennis Noll, Yannik Alexander Rath, Marcel Rieger, Erwin Rudi, and David Josef Schmidt
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16:45 |
T 71.4 |
AI-safety for jet flavour tagging at the CMS experiment — Xavier Coubez, Nikolas Frediani, Spandan Mondal, Andrzej Novak, Alexander Schmidt, and •Annika Stein
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17:00 |
T 71.5 |
Charm jet identification and discriminator calibration with the CMS experiment — •Spandan Mondal, Xavier Coubez, Alena Dodonova, Luca Mastrolorenzo, Andrzej Novak, Andrey Pozdnyakov, and Alexander Schmidt
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17:15 |
T 71.6 |
Performance Studies of the Integration of a Deep-Impact-Parameter-Setsbased Tagger for the ATLAS Experiment b-Tagging Algorithm — •Alexander Froch, Manuel Guth, and Andrea Knue
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17:30 |
T 71.7 |
Training of an extended b-tagging algorithm with deep neural networks. — •Thea Engler, Manuel Guth, Gregor Herten, and Andrea Knue
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17:45 |
T 71.8 |
Treating Uncertainties with Bayesian Neural Networks in a ttH Measurement — •Nikita Shadskiy and Ulrich Husemann
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18:00 |
T 71.9 |
Improvement of the jet-parton assignment in ttH(bb) events using machine-learning techniques — •Daniel Bahner, Andrea Knue, and Gregor Herten
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